Exploring Machine Learning Algorithms for Early Diagnosis of Plant Disease

Plant leaf diseases threaten agricultural productivity and food security, affecting global crop production. Traditional detection methods are often time-consuming and error-prone. This paper explores using machine learning to automate plant leaf disease detection, improving speed and accuracy. Paper elaborates the execution of a comprehensive database of plant leaf diseases, detailing symptoms, environmental conditions, and affected species, to train. The experimental results generated through the implementation were tested with the state-of-the-art methodologies and found to be over 90% accurate. The proposed method outperformed the existing methods. The performance measures like the accuracy and other evaluation metric proved the efficiency of the methodology presented with improved accuracy. The outcome of the paper served as a socio-beneficial so as the reformation of agricultural practices have been emerged out. The new revolution in the modern agricultural practices had emerged owing to the inclusion of machine learning (ML) and the Deep Learning models in the early disease prediction in plant.